HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
Auto-RAG: Autonomous retrieval- augmented generation for large language models
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A stateful iterative RAG system converts retrieved documents into scored reasoning units, maintains supportive and non-supportive evidence, and performs deficiency-driven query refinement to achieve more robust QA performance.
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HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
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Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning
A stateful iterative RAG system converts retrieved documents into scored reasoning units, maintains supportive and non-supportive evidence, and performs deficiency-driven query refinement to achieve more robust QA performance.